Float to Fix conversion

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1 Float to Fix conversion Fabrice Lemonnier Research & Technology

2 2 / Thales Research & Technology : Research center of Thales Objective: to propose technological breakthrough for the future products of Thales TRT Direction (E. Lansard) Research Groups Laboratories Sciences et Techniques de L Information Reasoning and analysis for complex system Decision & Optimisation Software system Engineering High Performance Computing Lab Safety for embedded system Lab Physics Waves and Signal Processing Micro-nano physics UMR CNRS- TRT Technology & Characterisatio n Submicron techno & process Nanocomposite & multifunction materials Organic materials chemistry Physics analysis Industrial process & technology analysis Technological demonstrators III-V Lab

3 3 / Applications Key issues Algorithms becoming dynamic and irregular, solve the issue of reconfigurable computing. Emerging algorithms in sensors raise technical challenges to architectures : beyond Von Neuman, beyond Moore. Applications become a mixture of computing levels (data flow, control) Drastic increase of data bandwidth out of the sensors Cognitive radio Design methodology Smart camera Drone Improve the link between algorithms and architecture Modularity and reuse Reliability Sub-micronic technologies are less and less reliable

4 4 / Rational The best trade off to raise the computing power for a low power consumption is obtained through: Parallelisation Customisation Australian Desert Animal: the Thorny Devil In the same time, we have to keep in mind the necessity of flexibility and programming efficiency.

5 5 / FPGA Why FPGA technology? High throughput Low power consumption (no compliant with GPGPU) Problem: floating-point computation is not efficient on FPGA (ratio of 5) The architecture design has to be in fixed-point BUT: the applications are generally coded in floating-point double precision The application has to be converted from floating-point to fixed-point: important impact on development flow (TTM, NRC)

6 6 / Fixed-point Arithmetic: Viewpoint from Industry Reduce development cost on FPGAs and MPSoC without floating-point unit Today this task is done by hand and can cost up to 6 man-months Avoid reject of designing efficient hardware accelerators on FPGAs Fixed-point arithmetic brings clear advantages in Area, speed, power, communication bandwith

7 7 / Architectures on FPGA Hardcoded IPs Necessary when volume of data is too important or latency is too short Conversion from floating-point to VHDL representation Dedicated processors When possible, dedicated processors are better due to programming efficiency Conversion from floating-point to assembly code

8 8 / core: an accelerator for Image processing SIMD : The sequencer execute the microcode All PEs (based on MULACC operator) execute the same instruction sequencer PE Local RAM PE Local RAM PE (Processing Element) : ALU based on a MULACC R A M PE PE PE Local RAM Local RAM Local RAM Local RAM (512 registers) 2 lines of the RAM PE Local RAM PE Local RAM Accelerator : SIMD Computing power : 50 Gops on Xilinx Virtex-5 SX240 Consumption : ~15W

9 9 / core: an accelerator for Image processing Common functions necessary to use an accelerator : NoC IF control data DMA Local Memory INoC sequencer PE PE CTR Local RAM Local RAM DMA to transfer data be computed CTR (controler) to execute the correct scheduling between data transfers and works Local Memory R A M PE PE PE Local RAM Local RAM Local RAM PE PE Local RAM Local RAM Accelerator : SIMD

10 10 / TeraTS: Application domains for Thales Airborne System Airborne radar Future requested power computing: Hundreds of Gops STAP algorithm (Space Time Adaptive Processing) high volume of data need of external memory Issue on bandwidth Electronic Warfare Future required computing power: Hundreds of Gops per channel computing directly in the data flow with a short latency : few µs High frequency Small array of data

11 11 / TeraTS: Solution Application high level representation Performance and flexibility through signal processing dedicated programmable accelerator FPGA RAM system level mapping GPP Hosting Structure Application executed on a GPP calling intensive computing operators on the accelerator Productivity through DDR I/Os D M A DSP Engine SEQUENCER PE0 PE1 Signal PE2 Processing PE3 IMEM Parallel Memory PE4 Programmable PE5 Accelerator PE6 PE7 Tool for mapping and parallelisation of the application and code generation Compiler toolset to generate the library of operators from C representation library of signal processing operators Compiler / assembler tools C code Xilinx Virtex-6 SX315 Consumption : ~50W

12 12 / PE of TeraTS The PE is a 32 bits processor Register file DSP ALU DSP ALU Link for complex operations

13 13 / TeraTS Programming tools Application High level representation Signal Processing operators in C language SPEAR DE (Parallelisation, mapping and code generation) compiler VLIWiser assembler C with Accelerator calls µblaze µcode Accelerator

14 14 / Application development impacts

15 15 / Space Time Adaptive Processing Objectives: remove clutter (ground reflexion) and detect moving targets.

16 16 / STAP principles V carrier pulses (recurrences) 5 antenna sub arrays v ground Surveillance of the ground by air: Detection of Moving Targets STAP computes dynamically the best filter to suppress the clutter (ground reflexion) and detect the moving targets ant range gates beam width rec rg An aircraft illuminates the ground, with a beam orthogonal to its velocity, by sending repeatedly sequences of periodic pulses (denoted rec) The echoed signal is received on 5 sensors (ant) The received signal is sampling at a given frequency, each sample corresponding to a distance called a range gates (rg) (typically 15 meters for a 10 MHz sampling)

17 17 / STAP algorithm Filters calculation Stimuli generation Filters application thresholding normalization ambiguity removal

18 18 / Programming flow Study Model Output PATTERN Matlab Ideal reference Model Output PATTERN Constrainted reference Model Intègre les caractéristiques de la cible (Tuile) Output PATTERN Environnement de Développement SPEAR Code generation Executable code

19 19 / Constrainted reference model Fixed-point arithmetic compliant with the constraints of the target: Multiplication, addition : 32 bits Accumulation: 70 bits Barrel shifter Signal noise ratio No overflow This conversion can be very long. It requests communication between engineers who don t speak the same language: Algorithm -> software -> hardware

20 20 / Fixed-Point Conversion Loss of precision incurs loss of performance Essentially, an optimization process Find trade-off between accuracy and cost Determine the number of bits for each data Manual conversion is tedious Strong need of tools Area Power Speed Performance Degradation

21 21 / Current Limit of Commercial tools Accuracy evaluation is performed using bit-true simulations Fixed-point simulation is very long Word-length optimisation time is prohibitive Used in all existing tools HDL coder Matlab (Mathworks), Vivado (Xilinx), Catalytic (Mentor Graphics) Strongly user-guided iterative process with long simulations in the loop It is the reason why we are involved in DEFIS project (ANR)

22 22 / DEFIS Aim of the DEFIS project is threefold To provide new methods for fixed-point conversion Analytical and efficient simulation-based methods To develop a complete software infrastructure for automatic fixedpoint conversion To demonstrate the quality of DEFIS flow on two industrial applications DEFIS at a glance Nov to Feb (40 months), today T0+19 Po le Images & seaux, Po le 2 PhD grants, 1 Engineer/PostDoc (36 months) , 281 person.months

23 XML This document is not to be reproduced, modified, adapted, published, translated in any material form in whole or in part nor disclosed to any third party without the prior written permission of 23 / System-Level Optimizations Spear DE (Thales) Accuracy constraint Software Integration FIPOGEN (LIP6) CGPE (LIRMM) Word-Length Optim. ID.Fix_FixConv (IRISA) Future modules ID.Fix_AccEval (IRISA) Fluctuat Error (CEA) Accuracy Evaluation GECOS (IRISA) Sardana (LIRMM) Parse Model (IRISA/CEA) Algorithm-Level Optimizations ID.Fix_DynEval (IRISA) Fluctuat Range (CEA) Stat_TVE (LIP6) Dynamic Range Evaluation Code generation (IRISA/CEA) DEFIS Software Infrastructure

24 24 / Perspectives Insertion in the development flow Reduction of the development cost Avoid risks when using dedicated accelerators based on fixed-point ALU

25 25 / Thank you for your attention! Questions

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